CN114298642A - Method for extracting urban truck trip OD from trajectory data - Google Patents
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Abstract
The invention provides a method for extracting an urban truck trip OD from trajectory data. The method comprises the following steps: determining a speed threshold of a truck in the city, and identifying a parking point from a track of the truck according to the speed threshold; sequencing the parking points of the truck according to the ascending sequence of the parking time, and determining a multi-stage time threshold value by drawing a Lorentz curve of the parking time; measuring the roundabout degree of a single trip path of the truck according to the multi-stage time threshold, and extracting potential trip OD points from truck parking points; and eliminating temporary parking points in the potential travel OD points, and extracting the real travel OD points of the truck. The invention provides a dynamic identification method for truck trip OD points, which is applicable to all cities across the country. The method has strong mobility, low complexity and easy realization.
Description
Technical Field
The invention relates to the technical field of urban freight management, in particular to a method for extracting an urban truck travel OD from trajectory data.
Background
In the urban goods transportation system, the truck transportation plays a key supporting role and is the main transportation form among large industrial enterprises, logistics warehouses and ports. However, the transportation by truck also causes serious social and environmental problems, such as traffic accidents, air pollution and the like, and brings challenges to the sustainable development of cities. In order to eliminate the negative externality of urban freight to improve the efficiency of freight systems, relevant departments and organizations need to develop viable freight policies. Large-scale freight train travel OD (Origin to Destination) information is basic data for formulating these freight policies, and provides data support for deep understanding of urban freight systems.
Traditionally, truck trip OD information is obtained through traffic surveys. This approach is time consuming and costly, and therefore the amount of data acquired is limited and not sufficient for urban freight system analysis. In the big data era, the development and application of satellite positioning technology provide possibility for acquiring large-scale truck track data through vehicle-mounted positioning equipment. However, how to extract the truck trip OD information from the trajectory data is a big problem in practice and has not been solved well. At present, the OD extraction of truck trips is less researched, and the following methods are mainly used:
in the prior art, a method for extracting a truck trip OD comprises the following steps: and identifying the type of the truck stopping point by auxiliary information such as truck driver survey data, land utilization data and the like. The disadvantages of this method are: although a good identification effect can be achieved when small samples are researched, the method is poor in portability due to the limitation of the data size of auxiliary information.
Another method for extracting a truck trip OD in the prior art is as follows: the method has the defects that the OD information of the real trip of the truck is required to be known in advance as a test set, and the information is difficult to directly acquire in practice.
Disclosure of Invention
The embodiment of the invention provides a method for extracting the travel OD of a truck in a city from track data so as to effectively and dynamically identify the travel OD of the truck.
In order to achieve the purpose, the invention adopts the following technical scheme.
The method for extracting the urban truck trip OD from the trajectory data comprises the following steps:
determining a speed threshold of a truck in the city, and identifying a parking point from a track of the truck according to the speed threshold;
sequencing the parking points of the truck according to the ascending sequence of the parking time, and determining a multi-stage time threshold value by drawing a Lorentz curve of the parking time;
measuring the roundabout degree of a single trip path of the truck according to the multi-stage time threshold, and extracting potential trip OD points from truck parking points;
and eliminating temporary parking points in the potential travel OD points, and extracting the real travel OD points of the truck.
Preferably, the sorting the parking spots of the trucks according to the ascending order of parking time, and determining the multi-stage time threshold by drawing a lorentz curve of the parking time includes:
respectively calculating the average speeds of two continuous GPS points in the GPS tracks of all trucks in the city to obtain the distribution of the average speeds of all trucks, wherein the distribution of the average speeds of the trucks is the mixed distribution of data drift speed and normal running speed of the trucks, and the probability distribution of the mixed distribution is as follows:
wherein, Lognorm (x; mu)1，σ1) The probability density function is lognormal distribution and is used for fitting the speed data of data drifting; norm (x; mu)2，σ2) Is a normally distributed probability density function which is used for fitting the speed data of normal running of the truck and estimating a parameter omega of mixed distribution by utilizing a maximum likelihood method1,ω2,μ1,μ2,σ1And σ2；
A speed value corresponding to a lowest point between two peaks of the mixing profile is determined as the speed threshold. If the speed of the truck at a location is less than the speed threshold, the location is identified as a stop, and the geographic coordinates of a truck stop are represented by the average of the longitude and latitude of all GPS points at the stop.
Preferably, the sorting the parking spots of the trucks according to the ascending order of parking time, and determining the multi-stage time threshold by drawing a lorentz curve of the parking time includes:
sequencing all parking points of the truck according to the ascending sequence of the parking time, drawing a Lorentz curve of the parking time, calculating the intersection point of a tangent line at the rightmost end point of the Lorentz curve and an x axis, determining the parking time of the truck corresponding to the intersection point as a time threshold, redrawing the Lorentz curve and calculating the intersection point of the tangent line and the x axis for the parking point with the parking time less than the time threshold, determining the time threshold of the next level, and continuously and iteratively executing the processing until the Lorentz curve is a straight line to obtain a multi-level time threshold.
Preferably, the measuring the detour degree of the single travel path of the truck according to the multi-stage time threshold value and extracting potential travel OD points from truck parking points include:
calculating the first K shortest paths from the starting place to the destination of each trip on a road network, finding out the nth shortest path closest to the actual trip path of the truck, measuring the roundabout degree of the single trip path of the truck according to the nth shortest path, and dynamically adjusting the time threshold by taking the roundabout degree of the single trip path of the truck as a reference;
selecting a maximum time threshold, identifying a parking point with parking time larger than the maximum time threshold as a potential travel OD point, dividing an original track into a plurality of sections of sub-tracks according to the potential travel OD point, naming the sub-track as a first-stage sub-track, calculating an nth shortest path between a departure place and a destination of each section of the first-stage sub-track, and if the length of a certain section of the first-stage sub-track is larger than the calculated nth shortest path, indicating that the section of the first-stage sub-track is roundabout more than a single travel path and comprises the travel OD point with short parking time; and then, selecting a next-stage time threshold value to identify travel OD points contained in the roundabout first-stage sub-track, dividing the first-stage sub-track into a plurality of sections of second-stage sub-tracks in one step, and continuously iterating the processing process until the sub-tracks cannot be divided, thereby indicating that all potential travel end points are extracted.
Preferably, the eliminating of the temporary stop point in the potential travel OD points and the extracting of the real travel OD point of the truck include:
judging whether the truck stays on the road for a long time due to traffic jam or not by using road network data, and if a certain potential travel OD point is located on the road, indicating that the potential travel OD point is a temporary parking point and needs to be removed; judging whether the truck loads and unloads goods in a freight enterprise or not by using the interest point data related to the freight in the city, and if the identified potential travel OD point is not located in the freight enterprise, indicating that the potential travel OD point is a temporary parking point and needs to be removed; otherwise, the potential travel OD point is a real travel OD point;
after the real travel OD point of the truck is extracted from the track data, the travel path of the truck is extracted by combining the GPS data, and the relevant travel index is calculated.
According to the technical scheme provided by the embodiment of the invention, the dynamic identification method for the truck trip OD point is provided, and is applicable to all cities nationwide. The method has strong mobility, low complexity and easy realization.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a processing flow chart of a method for extracting an in-city truck travel OD from trajectory data according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a speed threshold determination provided by an embodiment of the present invention;
fig. 3 is a schematic view illustrating a truck parking spot identification according to an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating a multi-level time threshold determination according to an embodiment of the present invention;
fig. 5 is a schematic diagram illustrating an nth shortest path determination according to an embodiment of the present invention;
fig. 6 is a schematic diagram of potential travel endpoints extracted from truck parking spots according to an embodiment of the present invention;
fig. 7 is a schematic diagram of removing temporary parking spots according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
The embodiment of the invention extracts the urban truck trip OD from the large-scale track data, and mainly comprises the following steps: determining a speed threshold value suitable for a certain city according to the distribution of the speed of the truck, and identifying a parking point from the original track of the truck; determining a plurality of time thresholds according to the distribution of the residence time of the truck at the parking point, and measuring the roundabout degree of the single trip path of the truck; dynamically selecting a time threshold value with a proper level to identify potential travel OD points from all parking points on the basis of the roundabout degree of a single travel path; and removing temporary stopping points from potential travel OD points by using the urban interest point data and the road network data, and further identifying all real travel OD points.
A processing flow chart of the method for extracting the urban truck trip OD from the trajectory data according to the embodiment of the present invention is shown in fig. 1, and includes the following processing steps:
step S10: a speed threshold for the urban trucks is determined, and a stopping point is identified from the truck trajectory.
Fig. 2 is a schematic diagram for determining a speed threshold according to an embodiment of the present invention, which is used for calculating average speeds of two consecutive GPS points in a GPS (Global Positioning System) track of each truck, so as to obtain distribution of average speeds of all trucks in a city. The distribution of the average speed of the truck is the mixed distribution of the data drift speed and the normal running speed of the truck, and the average speed distribution is obtained by utilizing the mixed distribution fitting of the formula (1).
The data drift speed means that the GPS points of the stationary vehicle can drift due to the possible deviation of the positioning of the GPS equipment, and the average speed of two continuous GPS points is not 0. Therefore, the invention distinguishes the data drift speed and the normal running speed of the truck by setting a speed threshold value so as to identify the real parking point of the truck. If the average speed of two continuous GPS points is less than the set speed threshold value, the truck is in a static state, and a parking point can be identified.
The probability distribution of the mixture distribution is as follows:
wherein, Lognorm (x; mu)1，σ1) The probability density function is lognormal distribution and is mainly used for fitting the speed data of data drifting; norm (x; mu)2，σ2) The probability density function is normally distributed and is mainly used for fitting the speed data of the normal running of the truck. Estimating parameter omega of mixed distribution by using maximum likelihood method1,ω2,μ1,μ2,σ1And σ2。
Fig. 3 is a schematic diagram of truck parking point identification provided by an embodiment of the present invention, and a speed value corresponding to a saddle point (i.e., the lowest point between two peaks in fig. 3) of a mixed distribution is determined as a speed threshold. If the speed of the truck at a location is less than the determined speed threshold, the location is identified as a stopping point. The geographic coordinates of a truck parking spot are represented by the average of the longitude and latitude of all GPS points at that parking location.
Step S20: and sequencing the parking points of the truck according to the ascending sequence of the parking time, and determining a multi-stage time threshold value by drawing a Lorentz curve of the parking time.
Fig. 4 is a schematic diagram illustrating a multi-level time threshold determination method according to an embodiment of the present invention, where a non-parametric iterative method is used to determine the multi-level time threshold. Firstly, sequencing the parking points of the truck according to the ascending sequence of the parking time, and drawing a Lorentz curve of the parking time; then, calculating an intersection point of a tangent line at the rightmost end point of the lorentz curve and the x axis, and determining the truck parking time corresponding to the intersection point as a time threshold, as shown in fig. 4 a; then, for the parking point with the parking time less than the time threshold, redrawing the lorentz curve and calculating the intersection point of the tangent line and the x axis, so as to determine the time threshold of the next level, as shown in fig. 4 b; this process is iterated until the lorentz curve is a straight line, as shown in fig. 4 h. After the iteration process is finished, a multi-level time threshold may be obtained for identifying potential travel OD points in step S30.
In the process of identifying potential travel OD points, the time threshold needs to be dynamically adjusted. The detouring degree of the single travel path of the truck is a reference for dynamically adjusting the time threshold. In order to measure the roundabout degree of a single travel path of a truck, firstly, the OD points of a part of the truck are extracted from original GPS data by using satellite images and city POI (point of interest) to serve as a sample data set. The OD point in the sample data set of the actual trip of the truck is manually extracted from the GPS data by using the satellite image and the city POI and is used as sample data for determining the algorithm parameters. These manually extracted OD points are a small part or may be understood as a subset of the real travel OD points extracted in step S40. The location associated with the truck event is typically a distinctive architectural feature that can be identified from the satellite image, so a small fraction of the truck OD points can be extracted from the GPS data using manual methods. However, most truck OD points are not geographically distinct and manual methods are time consuming and therefore not suitable for large scale GPS data. This is also the actual background considered by the present invention.
Then, calculating the front K shortest paths from the departure place to the destination of each trip on a road network; and then finding out the nth (n is less than or equal to K) shortest path closest to the actual travel path of the truck, and measuring the roundabout degree of the single travel path of the truck. Fig. 5 is a schematic diagram of an nth shortest path determination according to an embodiment of the present invention.
Step S30: and extracting potential travel OD points from the truck parking points according to the multistage time threshold and the nth shortest path.
Fig. 6 is a schematic diagram of extracting potential travel endpoints from truck parking spots according to an embodiment of the present invention. The method adopts a time threshold dynamic adjustment method to extract potential travel OD points from the parking points identified in the step S10. Firstly, selecting the maximum time threshold determined in step S20, identifying a parking point with parking time greater than the time threshold as a potential travel OD point, and dividing the original trajectory into a plurality of sub-trajectories according to the travel end points, which are named as primary sub-trajectories, as shown in the second layer of fig. 6; then, the nth shortest path from the starting place to the destination of each segment of the primary sub-track is calculated. If the length of a certain section of first-stage sub-track is greater than the calculated nth shortest path, the section of first-stage sub-track is more roundabout than a single travel path, namely a travel OD point with shorter parking time can be contained; then, selecting a next-stage time threshold to identify a travel OD point contained in the roundabout first-stage sub-track, and dividing the first-stage sub-track into a plurality of sections of second-stage sub-tracks in one step, as shown in the third layer of fig. 6; the above process is iterated until the sub-track cannot be segmented, which indicates that all potential travel endpoints are extracted, as shown in the fourth layer of fig. 6.
Step S40: and eliminating temporary stopping points in the potential travel OD points, and extracting real travel OD points.
Fig. 7 is a schematic diagram of removing temporary parking spots according to an embodiment of the present invention. The potential travel endpoints identified in step S30 may include long-term temporary stopping points, such as long-term traffic congestion points and rest points for drivers, which need to be eliminated to improve the accuracy of the method. First, it is determined whether the truck stays on the road for a long time due to traffic congestion using the road network data. If a potential travel OD point is located on the road, it indicates that the potential travel OD point is a temporary parking point and needs to be removed, as shown in fig. 7 a. And then, judging whether the truck is loaded or unloaded in the freight enterprise by using the point-of-interest data related to the freight in the city. If the identified potential travel OD point is not located in the freight transportation enterprise, the potential travel OD point is indicated to be a temporary parking point and needs to be removed, as shown in FIG. 7 b; otherwise the potential travel OD point is the true travel OD point, as shown in fig. 7 c.
After the real travel OD point of the truck is extracted from the track data, the travel path of the truck can be extracted by combining GPS data, and travel related indexes such as travel distance, travel time, loading and unloading time and the like can be calculated.
Table 1 shows a comparison of the process of the present invention with existing processes. Method accuracy rate MaccThe calculation is as follows:
Macc＝NA/(NA+NM+NU) (2)
NA represents the number of travel OD points that are accurately identified, and NM represents the number NU of travel OD points that are erroneously identified, which represents the number of travel OD points that are not identified. The result shows that the accuracy of the method provided by the invention is obviously higher than that of the prior art.
In summary, the embodiment of the present invention provides a method for determining a data-driven speed threshold, which is more accurate and objective and has universality. The possibility of the parking spot being recognized by mistake is greatly reduced.
The invention provides a data-driven multistage time threshold determination method which is accurate, objective and strong in universality. The temporary parking spots are prevented from being recognized as the parking spots by mistake, and the accuracy of the method is improved.
The invention provides a method for processing temporary parking spots by using urban interest point data and road network data which can be widely obtained, and the accuracy of the method is improved.
The invention provides a dynamic identification method for truck trip OD points, which is applicable to all cities across the country. The method has strong mobility, low complexity and easy realization.
TABLE 1 comparison of the accuracy of the method of the invention with that of the prior art
Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
From the above description of the embodiments, it is clear to those skilled in the art that the present invention can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, they are described in relative terms, as long as they are described in partial descriptions of method embodiments. The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (5)
1. The method for extracting the urban truck trip OD from the trajectory data is characterized by comprising the following steps:
determining a speed threshold of a truck in the city, and identifying a parking point from a track of the truck according to the speed threshold;
sequencing the parking points of the truck according to the ascending sequence of the parking time, and determining a multi-stage time threshold value by drawing a Lorentz curve of the parking time;
measuring the roundabout degree of a single trip path of the truck according to the multi-stage time threshold, and extracting potential trip OD points from truck parking points;
and eliminating temporary parking points in the potential travel OD points, and extracting the real travel OD points of the truck.
2. The method of claim 1, wherein the sorting of the stops of the trucks in ascending order of stop time, and the determining the multi-level time threshold by plotting a Lorentzian curve of the stop time, comprises:
respectively calculating the average speeds of two continuous GPS points in the GPS tracks of all trucks in the city to obtain the distribution of the average speeds of all trucks, wherein the distribution of the average speeds of the trucks is the mixed distribution of data drift speed and normal running speed of the trucks, and the probability distribution of the mixed distribution is as follows:
wherein, Lognorm (x; mu)1，σ1) The probability density function is lognormal distribution and is used for fitting the speed data of data drifting; norm (x; mu)2，σ2) Is a normally distributed probability density function which is used for fitting the speed data of normal running of the truck and estimating a parameter omega of mixed distribution by utilizing a maximum likelihood method1，ω2，μ1，μ2，σ1And σ2；
A speed value corresponding to a lowest point between two peaks of the mixing profile is determined as the speed threshold. If the speed of the truck at a location is less than the speed threshold, the location is identified as a stop, and the geographic coordinates of a truck stop are represented by the average of the longitude and latitude of all GPS points at the stop.
3. The method of claim 1, wherein the sorting of the stops of the trucks in ascending order of stop time, and the determining the multi-level time threshold by plotting a Lorentzian curve of the stop time, comprises:
sequencing all parking points of the truck according to the ascending sequence of the parking time, drawing a Lorentz curve of the parking time, calculating the intersection point of a tangent line at the rightmost end point of the Lorentz curve and an x axis, determining the parking time of the truck corresponding to the intersection point as a time threshold, redrawing the Lorentz curve and calculating the intersection point of the tangent line and the x axis for the parking point with the parking time less than the time threshold, determining the time threshold of the next level, and continuously and iteratively executing the processing until the Lorentz curve is a straight line to obtain a multi-level time threshold.
4. The method according to claim 3, wherein said extracting potential travel OD points from truck stop points according to said measure of detour of single travel path of truck by said multi-stage time threshold comprises:
calculating the first K shortest paths from the starting place to the destination of each trip on a road network, finding out the nth shortest path closest to the actual trip path of the truck, measuring the roundabout degree of the single trip path of the truck according to the nth shortest path, and dynamically adjusting the time threshold by taking the roundabout degree of the single trip path of the truck as a reference;
selecting a maximum time threshold, identifying a parking point with parking time larger than the maximum time threshold as a potential travel OD point, dividing an original track into a plurality of sections of sub-tracks according to the potential travel OD point, naming the sub-track as a first-stage sub-track, calculating an nth shortest path between a departure place and a destination of each section of the first-stage sub-track, and if the length of a certain section of the first-stage sub-track is larger than the calculated nth shortest path, indicating that the section of the first-stage sub-track is roundabout more than a single travel path and comprises the travel OD point with short parking time; and then, selecting a next-stage time threshold value to identify travel OD points contained in the roundabout first-stage sub-track, dividing the first-stage sub-track into a plurality of sections of second-stage sub-tracks in one step, and continuously iterating the processing process until the sub-tracks cannot be divided, thereby indicating that all potential travel end points are extracted.
5. The method according to claim 4, wherein the step of eliminating the temporary stop points in the potential travel OD points and extracting the real travel OD point of the truck comprises:
judging whether the truck stays on the road for a long time due to traffic jam or not by using road network data, and if a certain potential travel OD point is located on the road, indicating that the potential travel OD point is a temporary parking point and needs to be removed; judging whether the truck loads and unloads goods in a freight enterprise or not by using the interest point data related to the freight in the city, and if the identified potential travel OD point is not located in the freight enterprise, indicating that the potential travel OD point is a temporary parking point and needs to be removed; otherwise, the potential travel OD point is a real travel OD point;
after the real travel OD point of the truck is extracted from the track data, the travel path of the truck is extracted by combining the GPS data, and the relevant travel index is calculated.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114863715A (en) * | 2022-05-05 | 2022-08-05 | 一汽解放汽车有限公司 | Parking data determination method and device, electronic equipment and storage medium |
| CN116029624A (en) * | 2022-07-21 | 2023-04-28 | 大连海事大学 | Goods source place identification method integrating truck track and POI data |
| CN116434529A (en) * | 2022-12-12 | 2023-07-14 | 交通运输部规划研究院 | Inter-city highway freight characteristic analysis method and device and electronic equipment |
| CN117173898A (en) * | 2023-11-03 | 2023-12-05 | 深圳市城市交通规划设计研究中心股份有限公司 | Method for extracting travel OD (optical density) based on parking lot flow data in time-division and destination-division manner |
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- 2021-12-31 CN CN202111669404.6A patent/CN114298642A/en active Pending
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114863715A (en) * | 2022-05-05 | 2022-08-05 | 一汽解放汽车有限公司 | Parking data determination method and device, electronic equipment and storage medium |
| CN116029624A (en) * | 2022-07-21 | 2023-04-28 | 大连海事大学 | Goods source place identification method integrating truck track and POI data |
| CN116029624B (en) * | 2022-07-21 | 2024-02-06 | 大连海事大学 | Goods source place identification method integrating truck track and POI data |
| CN116434529A (en) * | 2022-12-12 | 2023-07-14 | 交通运输部规划研究院 | Inter-city highway freight characteristic analysis method and device and electronic equipment |
| CN116434529B (en) * | 2022-12-12 | 2023-10-24 | 交通运输部规划研究院 | Inter-city highway freight characteristic analysis method and device and electronic equipment |
| CN117173898A (en) * | 2023-11-03 | 2023-12-05 | 深圳市城市交通规划设计研究中心股份有限公司 | Method for extracting travel OD (optical density) based on parking lot flow data in time-division and destination-division manner |
| CN117173898B (en) * | 2023-11-03 | 2024-02-27 | 深圳市城市交通规划设计研究中心股份有限公司 | Method for extracting travel OD (optical density) based on parking lot flow data in time-division and destination-division manner |
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